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• The model provides the best configuration to maximize crop yield and energy output. • The model gives the optimal height, spacing between tables, table size, and tilt. • The amount of solar irradiation available for crops under the panels are calculated. • The energy output and crop yield for each configuration of the system is evaluated. • The increase in crop yield is more sensitive to the expansion of panel row spacing. The global population is experiencing rapid growth, leading to increased demand for energy and food resources, necessitating the expansion of cultivated land. The construction of photovoltaic power plants to meet energy needs may result in competition for land between the agriculture and energy sectors. To address this issue, agrivoltaics systems are perceived as a solution, allowing for the coexistence of agricultural and energy production in the same area. However, the shading caused by solar panels can potentially. Therefore, a model has been developed to determine the best configuration for maximising both crop yields and energy production from the photovoltaic field. The purpose of this paper is to develop a model that optimizes energy production and crop yield within an agrivoltaics system. The model integrates factors such as elevation, spacing, tilt, panel technology and size to enhance the radiation under the photovoltaic panels, as well as to increase crop yield and the efficiency of photovoltaic array. It is constructed based on the climatic condition and the relationship between the shaded area and the sunlight distribution below the photovoltaic panels. Furthermore, the model relies on the correlation between the configuration used and the energy power delivered by the photovoltaic array. A set of equations that link configuration, sunlight, crop yield, and photovoltaic panel power was developed, and the model was implemented in MATLAB, using genetic algorithm optimisation techniques. The initial step involves the determination of radiation values under the panels, followed by the identification of the best scenarios for subsequent simulations aimed at evaluating crop yield and power generation from the photovoltaic array. A case study was conducted in Kamboinsin village (12°27′ N, 1°33′ W), in Burkina Faso, focusing onusing corn cultivation to validate the model. The results show that the model effectively identifies the optimal configuration for maximizing both crop yield and photovoltaic field output. The simulation results reveals that the distribution of radiation under the panels is significantly influenced by factors such as panel elevation height, spacing between table, and spacing between rows of table. Notably, the yield is more sensitive to the spacing between rows of panels. When comparing the effects of the different panel sizes, it is evident that utilizing smaller tables leads to higher crop yields. However, this approach results in a decrease in energy production from the photovoltaic field. For instance, on 1 ha of land, a table consisting of a single 100 Wp panel generated 92.8 % of the crop yield achieved in full sun with a nominal power of 96.9 kWp, whereas a table comprising 2 panels of 260 Wp produced 80.1 % of the yield with a nominal power of 378.56 kWp. [ABSTRACT FROM AUTHOR]

Competitive and changing frame conditions, such as the need for decarbonization of energy supply or the growing share of renewable electricity generation, are driving the need for flexibility in different systems, e.g., in power supply and industrial production. Flexibility is often included in mathematical optimization applications. However, integrating it in optimization may create conflicting understandings of flexibility due to the properties of relativity and goal orientation. Establishing a common understanding of flexibility in industrial systems among all stakeholders, and a general but adaptable formulation for inclusion in mathematical optimization could better exploit the potential for flexibility in the future and make a greater contribution to energy transition. In this paper, an overview of flexibility, the incentives for it, and its integration into the decision variables, constraints, and objectives of mathematical optimization is provided. This enables the authors to conceptualize industrial flexibility in optimization and provide the basis for the presented approach: a three-step mixed-integer linear programming optimization model for integrating flexibility in the evaluation of cost-optimal energy supply systems. The presented approach is generally suitable for various flexibility types but is applied in this study to assess energy-source flexibility and elaborate its cost implications in a use case from the industry. For the identified energy supply systems with full energy carrier flexibility, the total annual costs increased by 62 to 112% compared to the cost-optimal solution with all available energy carriers, of which a relevant share was due to the investment cost increase by a factor of up to four. The two main conclusions are that (i) the results of the presented use case reveal the order of magnitude in cost increase for energy-source flexible supply systems, and (ii) the presented approach should be further used and developed to evaluate different types of industrial flexibility e.g., analysis of cost increase for robust energy systems in a changing environment, a potential contribution of flexibility for energy markets or power grids, or the flexibility to allow production changes. [Display omitted] • Interfaces of flexibility incentives and optimization for industry presented. • Set up of adaptable optimization framework for different flexibility aspects. • Cost increase quantified for energy-source flexibility in industrial use case. [ABSTRACT FROM AUTHOR]

• Vehicle speed computation designed to give driver indications based on target speed. • Target speed calculation based on Predictive Cruise Control for energy reduction. • Speed computation algorithm valid to any vehicle type and powertrain architecture. • Simulated energy reduction achieved on real scenarios between 3% and 11%. • Algorithm suitable for on board implementation and real time use on vehicles. Energy reduction in automotive applications is addressed since many years by both academia and vehicle manufactures. Recently, the introduction of innovative technical solutions, based, for instance, on internet connectivity and Advanced Driver-Assistance Systems (ADAS), allowed the on-board implementation of complex Energy Management Strategies (EMS) based on advanced optimization algorithms. However, these solutions require, in most cases, high computational capabilities and are generally customized on vehicle type and addressed to hybrid powertrain configurations. In this regard, this paper presents a vehicle speed management algorithm that suggests in real time to the driver the speed that shall be followed to achieve fuel consumption reduction. The algorithm requires road information, such as speed limits and slope variation, but it can be applied to any vehicle (e.g., light-weight cars, busses, trucks, etc.) and powertrain configuration (conventional, hybrid, pure electric, etc.). The basic structure relies on Predictive Cruise Control (PCC) approach, without introducing any complex mathematical optimization algorithm. The algorithm performance is here investigated in simulated environment by analyzing different vehicle types and routes. Under all the addressed scenarios, the algorithm can allow fulfilling energy consumption reduction (within the range of 3%-11% for real scenarios) with respect to a given reference speed profile under the same travel time. [ABSTRACT FROM AUTHOR]

Partial shading on photovoltaic (PV) modules significantly affects their productivity. Therefore, different PV reconfiguration approaches were employed as solutions for distributing the shade across the array. This paper proposes an advanced four-square (FS) sudoku technique for configuring the 9 × 9 total-cross-tied (TCT) PV array via moving the modules physically for one time to enhance the harvested maximum power under all possible shading patterns. The proposed FS is validated under different shading classes, including deterministic and random shading patterns. The performance of the proposed strategy is assessed versus six different approaches representing the literature, including basic sudoku (SDU), optimal sudoku (OSDU), chaos map (CMP), skyscraper (Sky-S), odd-even prime (OEP), and ancient chinese magic square (ACMS). The assessment process is conducted using the global maximum power point, execution ratio (ER), fill factor (FF), percentage power losses (% P L), power enhancement (% P H), and array output power-voltage (P-V) characteristic. Moreover, an extensive analysis of the cost and the energy saving during a day is performed to investigate the reliability of the proposed FS strategy. Furthermore, a critical evaluation is performed among the FS and recent dynamic reconfiguration approaches, including particle swarm optimizer (PSO), harris hawk optimization algorithm (HHO), genetic algorithm (GA), butterfly optimization algorithm (BOA), coyote optimization algorithm (COA) and artificial ecosystem-based optimization (AEO). The results of this paper reveal that the rearranged scheme-based FS approach can disperse different categories of shading efficiently with a high generated energy from the entire system and high revenue compared to TCT and other state-of-the-art arrangements with achieving smooth uni-peak P-V characteristics. • A novel static reconfiguration approach using four square sudoku (FS) is proposed. • FS is proposed as a universal scheme for dispersing several shade patterns. • FS is evaluated versus several static and dynamic reconfiguration approaches. • FS reliability is assessed by determining the energy savings over a day. • FS enhanced the power by 38.016% in the existence of random failed modules. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel optimization approach is developed for the Food-Energy-Water Nexus. • The energy and food industries exhibit synergy and competition for water use. • Synergy and competition scenarios quantify the state of industrial water use. • The inter-industry competitive and unsustainable water use are prominent. • Response paths are proposed based on administrative means and market allocation. Studies on the Food-Energy-Water Nexus can help researchers, policy makers, practitioners, and stakeholders identify opportunities to maintain the nexus' synergies and trade-offs. Water, the most sensitive element in the Food-Energy-Water Nexus, readily influences the stability, cooperativity, and safety of the nexus. The key initiative to ensure water security in the Food-Energy-Water Nexus is properly handling water for food and energy production, but the existed conceptual framework and evaluation system are incomplete. This paper uses the Driver-Pressure-State-Impact-Response model and the water footprint theory to construct an optimization approach to evaluate the synergy and competition for water between food and energy at five levels. This optimization approach was tested and implemented based on a case study of 31 provinces in the Chinese Mainland from 1997 to 2016. The results showed that the blue water footprint of 31 provinces was 263.48 Gm3 in 2016, and the gray water footprint was 1518.57 Gm3, which led to inter-industry competitive water use and water unsustainability. In 2016, the 31 provinces had developed into Industry Synergy Sustainability scenario (1 province), Industry Synergy Unsustainability scenario (9 provinces), Industry Competition Unsustainability scenario (16 provinces), and Industry Competition Sustainability scenario (5 provinces), presenting a spatially clustered distribution pattern. Except for Xinjiang and Jilin, the remaining 29 provinces gradually developed into sustainable or synergistic scenarios. The total production water footprint in the Industry Competition Unsustainability scenario reached 4.08 m3/kg in 2016, while the Industry Synergy Sustainability scenario was only 3.67 m3/kg. This paper proposes two response paths, based on market allocation and administrative means, to facilitate the gradual evolution of the Industry Competition Unsustainability scenario into the Industry Synergy Sustainability scenario. These paths contribute to the efficient and sustainable integrated management of food, energy, and water globally. [ABSTRACT FROM AUTHOR]

• Diagnostic approach to evaluate the health state of District heating Networks (DHNs). • Detection and identification of leakages, anomalous heat and pressure losses. • Detection and identification of single and multiple faults. • Validation of the diagnostic approach that correctly detects and identifies all implanted faults. District Heating Networks (DHNs) are composed of numerous pipes that can be threatened by faults that affect DHN operation and management. Thus, reliable diagnostic methodologies are essential to identify DHN health state and hinder DHN malfunctioning and performance deterioration. To this purpose, a novel diagnostic approach that couples a DHN simulation model with an optimization algorithm for detecting and identifying both thermal and hydraulic faults, i.e., water leakages, anomalous heat and pressure losses, is presented in this paper. In the current paper, the novel diagnostic approach is challenged at evaluating the health state of the DHN of the campus of the University of Parma, where different faults are artificially implanted, by using a digital twin of the DHN. The faulty datasets account for both single and multiple faults, as well as different fault types and causes. The novel diagnostic approach proves to correctly detect and identify all simulated faults, by also correctly estimating their magnitude even in the most challenging scenarios. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new improved optimization method is proposed. • The standard deviation of the optimal solutions decision variables increased by 70%. • The implementation of the optimal solutions has been improved. • The requirements of overall planners and implementers are comprehensively considered. The multiobjective optimization of the system usually focuses on the optimization of the objective functions while ignoring the influence of decision variables on the implementation of the solution. This paper proposes a new improved optimization method by embedding the decision variable diversification mechanism in the optimization process, adopting the discretization mechanism in the multisource complementary heating model, and improving the search space. The new improved optimization method and the original method have similar performance in obtaining the Pareto front, and the hypervolume of the two algorithms differs by only 1.37% on average. The standard deviations of the decision variables in the optimal solutions obtained by the improved algorithm are increased by 70%, and it has a higher diversity of solutions in the decision space. The equipment capacity obtained by the improved algorithm is discretized, and avoids equipment with lower capacity which is beneficial to construction. In this paper, the optimal implementation solution is obtained through the selection of the objective functions by the overall planners and the construction preference of the solution implementers. In this way, the overall planners' requirements for energy conservation, emission reduction and economy, as well as the solution implementers' choice of implementation solutions can be comprehensively considered. In addition, this paper also obtains another optimal solution for adopting the carbon pricing method. [ABSTRACT FROM AUTHOR]

• Lossless coupling of PV and TEG systems to enhance power generation efficiency. • An efficient SSA algorithm is used for MPPT of PV-TEG system. • Twelve methods are used as comparative algorithms. • Four case studies were used to validate the effectiveness of SSA. • Two constructive recommendations are proposed for future development. This paper proposes an innovative strategy to integrate thermoelectric generator (TEG) and photovoltaic (PV) systems, aiming to enhance energy production efficiency by addressing the significant waste heat generated during traditional PV system operation. Additionally, photovoltaic-thermoelectric generator (PV-TEG) hybrid system encounters the dual challenge of partial shading conditions (PSC) and non-uniform temperature distribution (NTD). Thus, salp swarm optimization (SSA) is introduced to simultaneously tackle the negative impacts of PSC and NTD. In contrast to alternative meta -heuristic algorithms (MhAs) and conventional mathematical approaches, the streamlined and effective optimization mechanism inherent to SSA affords a shorter optimization time, while mitigating the risk of the PV-TEG hybrid system's optimization outcomes being confined to local maximum power points (LMPP). Furthermore, the optimization performance of SSA for PV-TEG hybrid systems is assessed via four case studies, including start-up test, stepwise variations in solar irradiation at constant temperature, stochastic change in solar irradiation, and field measured data for typical days in Hong Kong, in which simulation results show that SSA evinces unparalleled global exploration and local search capabilities, yielding heightened energy output (up to 43.75%) and effectively suppressing power fluctuations in the PV-TEG hybrid system (as evidenced by Δ V avg and Δ V max). [ABSTRACT FROM AUTHOR]

[Display omitted] • A BOG reliquefaction system using liquid air was developed. • Mathematical optimization was performed to minimize annual expenses. • Techno-economic analysis was conducted for three different voyage duration scenarios. • The proposed system can reduce both onboard energy consumption and capital investment. The boil-off gas (BOG) reliquefaction of cryogenic liquefied natural gas (LNG) for its intercontinental transport is challenging. This paper proposes a novel BOG reliquefaction system using liquid air and two different process designs: (i) low-pressure liquid air (LPLA) and (ii) high-pressure liquid air (HPLA). In the proposed system, an open refrigeration system using liquid air reliquefies BOG, thereby replacing the current commercial closed refrigeration cycle. As the temperature of liquid air is in the cryogenic range, the energy consumption to reliquefy BOG onboard approaches zero. However, the additional total weight of the ship is required to store liquid air, thus necessitating increased propulsion energy. Nevertheless, the reduction in energy needed for BOG reliquefaction exceeds the energy requirement for the added engine load. Thus, the LPLA shows a massive energy reduction from 4,913.07 to 642.67 kW compared to the commercial BOG reliquefaction process involving the nitrogen expander cycle. The HPLA process shows further improved results of 436.34 kW by expanding the vaporized high-pressure air. In addition, the proposed HPLA design exhibits an exergy efficiency of 77.4 %, which is 37.6 % higher than that of the commercial nitrogen expander process. A techno-economic analysis, including the purchase and transport cost of liquid air, is conducted to reflect an effort on on-shore liquid air production and transportation. Despite the additional liquid air tanks, the overall process configuration is much simpler than that of the current commercial BOG reliquefaction system. Thus, the proposed HPLA process is more economical than commercial processes for all representative voyage duration scenarios (8, 13, and 21 days). [ABSTRACT FROM AUTHOR]

• Propose a local hybrid energy system based on solar energy for a residential district. • Apply BTES to store solar energy in non-heating seasons, and release in heating ones. • Apply genetic algorithms to optimize the system performance and costs. • 38%-58% of total heating demand can be covered by the optimal solutions. • Annually reduce CO2 emissions by 102–217 tons with the rate of 31–66% Utilizing solar energy for heat supply can reduce CO 2 emissions and mitigate global climate change. In the Nordic region (e.g., Iceland and Finland), a tremendous seasonal mismatch exists between the availability of solar radiation and building heating demand. This paper proposes a local hybrid energy system based on solar energy for a residential district. It applies a borehole thermal energy storage to store solar energy in non-heating seasons, and uses stored energy for part of total heating demand in a residential neighbourhood in heating seasons. Photovoltaic panels are used to generate electricity for heat pump operation. To find out cost-optimal and eco-friendly solutions, the local energy system was first modelled and simulated in TRNSYS. Then, genetic algorithms were applied to optimize the system performance and costs. In optimal solutions, 38%-58% of total heating demand could be covered by on-site heat energy with the levelized cost of energy of 110–184 €/MWh. On this basis, importing additional electricity from grid to increase the utilization rate of air-to-water heat pumps can further increase the on-site heat energy fraction to 41%-88% with the levelized cost of energy of 108–201 €/MWh. Compared with the situation of fully district heating input, the proposed system can annually reduce CO 2 emissions by 102–217 tons with the rate of 31–66%. Although the initial cost of the studied system is higher than that of district heating, the local hybrid energy system is worth further developing considering decentralizing heat energy production and reducing CO 2 emissions. [ABSTRACT FROM AUTHOR]

• A model predictive control based optimization framework for heat pump system of a residential district is proposed. • Heating load and indoor state is predicted by data-driven model as the input of the optimization process. • Data-driven models and traditional fitting models for system components are combined in the optimization process. • Onsite validation is conducted. Heat pump system is widely used in district heating because of its high energy efficiency and economic benefits. Because of the complexity of large heat pump systems, achieving optimal operation in practical project remains challenging. Model predictive control is a promising method for operation optimization. This paper represents a data-driven optimization framework based on model predictive control for optimal district energy supply. Four sub-models of the framework including heating load prediction model, heat pump performance model, main pump performance model and indoor state prediction model were built via machine learning and fitting methods. The particle swarm optimization algorithm was adopted to determine the optimal operation strategy. The framework' effectiveness was verified by the operation data of a district energy system in Shanghai by means of both offline and onsite validation. The optimization results under low load conditions show that the energy-saving ratio can reach 9.43%, correspondingly, 12.37% for medium load conditions and 4.50% for high load conditions. In onsite validation, the Mean Absolute Percentage Error and Root Mean Square Error of heating load prediction are 6.72% and 104.21 kW, respectively. Compared with the day under similar weather conditions, the framework helps operators to obtain 12.9% energy-saving ratio in practice. [ABSTRACT FROM AUTHOR]

• An isolated hybrid power system is simulated and analysed. • Optimal sizing and electricity demand with cost improvement. • Photovoltaic panels, biomass gasifier, and batteries units. • A Quantum model of a RUNge Kutta optimization algorithm. In this paper, an isolated hybrid power system (HPS) is simulated and analyzed to obtain the optimal sizing and meet the electricity demand with cost improvement for servicing Alrashda village, which is a small village of the Dakhla Oasis villages in the New Valley, Egypt. The major configuration of this HPS is Photovoltaic panels (PV), Biomass gasifier, and Batteries units. A Quantum model of a RUNge Kutta optimization algorithm called QRUN algorithm is applied to guarantee that all load needs are covered with high reliability with the minimum cost of energy (COE) of the proposed system. To ensure the superiority of the QRUN technique, its results are compared with those obtained by the original optimization algorithm RUNge Kutta optimization algorithm (RUN), and a well-known optimization algorithm, Aquila optimizer algorithm (AO) and Gray Wolf algorithm (GWO). The simulation results show that the modification QRUN algorithm gives the best Fitness Function with 0.080076 $/kWh of the COE, net present cost (NPC) with 3,352,062 $, and loss of power supply probability (LPSP) with 0.490866. [ABSTRACT FROM AUTHOR]

• The thermal economy model of combined cycle is investigated. • RBF-NSGA-Ⅱ optimization algorithm is proposed. • The functional relationship among exergy loss, cost and efficiency is established. This investigation proposed the RBF-NSGA-Ⅱ optimization algorithm and explored the thermal economy of different bottom combined cycles under 16 design parameters. The maximum efficiency, minimum exergy loss and minimum unit investment of different gas–water system combined cycles under various design factors were compared with traditional optimization, NSGA-Ⅱ optimization and RBF-NSGA-Ⅱ optimization. The results indicated that the combined cycle efficiency optimized by NSGA-Ⅱ and RBF-NSGA-Ⅱ increased by 25.2%, the exergy loss was reduced by 32%, and the specific investment was reduced by 38% compared with the traditional optimization. This investigation showed that the convergence speed of the newly proposed RBF-NSGA-Ⅱ optimization algorithm is increased by three times compared to the NSGA-Ⅱ optimization algorithm. Furthermore, through single-parameter sensitivity analysis, the relationship between compressor outlet pressure, gas turbine inlet temperature, pinch point temperature difference, approach point temperature difference, waste heat boiler pressure and combined cycle efficiency, exergy loss, and specific investment costs were investigated. The paper provided a new way for combined cycle design and guided the operation of combined cycle power plants. [ABSTRACT FROM AUTHOR]

• Multi-criteria optimisation is performed on an industrial thermocline tank. • Objective function includes exergy efficiency and a normalised environmental indicator. • Pareto set is obtained, bounded by the exergy and LCA-optimised solutions. • Exergy optimisation leads to a tapered tank shape. • Environmental optimisation leads to a square tank shape. Thermocline thermal energy storage is one of the most promising solutions for recovering waste heat in industrial plants. This paper aims to optimise the shape of a thermal energy storage to minimise its environmental impacts and maximise its exergy efficiency. The reference storage is an existing industrial high-temperature air/ceramic packed-bed heat storage called EcoStock®. The physical model used to determine the performances of the tank is a one dimensional model with two equations: one for the heat transfer fluid and one for the filler material. The environmental impacts are analysed using a life cycle assessment through four selected indicators: cumulative energy demand, global warming potential, abiotic depletion potential and particulate matter. To solve this multi-criteria problem, a particle swarm optimisation algorithm was applied with several exergy and environmental weighting factors. A Pareto set is obtained, bounded by the single exergy or environmental optimisations. Favouring exergy efficiency reduces the volume of the tank. However, environmental footprint of the tank is increased: the indicators of cumulative energy demand and abiotic depletion potential are considerably higher. The shape of the tank evolves with the exergy weight, from a square shape (environmental optimisation) to a tapered shape (exergy optimisation). [ABSTRACT FROM AUTHOR]

• Multiobjective optimization problem to optimally size hybrid-energy-source HEVs. • Battery, ultracapacitor, and fuel cell unit configurations as hybrid energy sources. • Storage size design to recover braking energy via UC/BU to reduce power loss. • Adaptive energy management strategy with quantum butterfly optimization algorithm. • Proposed strategy performs better than discrete wavelet transform power splitting. This paper proposes an optimal hybrid energy sources sizing methodology for hybrid electric vehicles comprising ultracapacitor (UC) and fuel cell (FC) with battery units (BU). For this purpose, a multi objective problem is formulated using dynamic-source models to evaluate the system's initial cost, weight, running cost, and cost associated with source degradation. Furthermore, a novel adaptive energy management strategy (AEMS) that focuses on dynamic-source characteristics and drive cycle power demand is proposed as an integral part of the optimization problem. Finally, to solve the hybrid energy source optimization problem, the butterfly optimization algorithm (BOA) is improved by employing the quantum wave concept to explore the search space more effectively. The performance of the proposed method is evaluated with different hybrid source configurations and various drive cycles using improved BOA, BOA and particle swarm optimization. The Matlab® simulation results show that battery rating can be downsized by approximately 40% upon the inclusion of UC and FC units using improved BOA. Furthermore, when the proposed AEMS is compared with a conventional discrete wavelet transform power-splitting approach used in the optimization process, the proposed AEMS performs better and could reduce the system relative cost and weight for BU-UC-FC configuration by 16% and 10% respectively. [ABSTRACT FROM AUTHOR]

• The performance of a hybrid solar chimney and desalination is analyzed using CFD. • Geometric optimization of the proposed system is conducted. • The results showed significant improvement when the chimney was divergent. • Optimum geometry's angles for chimney, desalination, collector are 1°, 8°, and 12°. In a world with ever-dwindling resources, attention is now being directed towards renewable energy systems. In this paper, a hybrid solar chimney and water desalination plant is simulated and optimized using CFD; giving special attention to the desalination. The objective of this study is to find the optimum geometric parameters and analyze their impact on electricity generation and freshwater production. Transient 2D numerical simulations in ANSYS Fluent, incorporating the discrete ordinates (DO) and volume of fluid (VOF), were performed for six different configurations of a full-scale system. The geometries have different inclination angles for the desalination roof (8° or 10°), the solar collector (8°, 10°, or 12°), and the chimney (0° or 1°). The behavior of the system was investigated using the velocity, temperature, and water desalination charts, which were obtained for a 12-h period. The results showed that a divergent chimney could increase the average air velocity from 17% to 23%, and increase the electricity generation from 64% to 95%. Furthermore, the models with a divergent solar collector could produce almost twice as much freshwater as the models with parallel collectors. The model with the best performance had a divergent chimney with an angle of 1 °, a desalination roof with an angle of 8 °, and a solar collector with an angle of 12 °. [ABSTRACT FROM AUTHOR]

[Display omitted] • Comparison of 24 mixed-integer linear programs for designing energy hubs. • Models are evaluated with detailed unit commitment optimization. • Part-load efficiencies lead to good designs but high computational costs. • Design heuristics with linear programs perform as well as the most complex models. Designing sustainable, cross-sectoral energy supply systems is a challenging task. A widespread and proven planning approach is mathematical optimization and in particular mixed-integer linear programming (MILP). While numerous MILP models have been presented in literature, there is no convention which level of detail is necessary to obtain reliable energy system designs. In this paper, a systematic performance comparison of 24 MILP models for designing multi-energy systems is conducted. The models include different combinations of five widely used model features: Piece-wise linear investment curves, multiple component resolution, minimum part-load limitations, part-load efficiencies, and start-up costs. The operational performances of the optimal system designs are compared by using a unit commitment optimization with high level of detail. In a district heating case study, the total annualized costs of the unit commitment optimizations differ substantially from 391 to 481 kEUR and the computation times of the design optimizations range from 10 s to more than 10 h. Models that consider part-load efficiencies lead to the lowest system costs but the highest computation times. In addition, simple design heuristics are identified which lead in combination with fast-solving linear models to energy systems with low total annualized costs (410 kEUR, 5% cost increase). [ABSTRACT FROM AUTHOR]